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description=Learn how to create and configure measures for quantitative analysis and business insights in your metrics views;

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define your measures rill skip to main content developers guide reference project files time syntax rill iso 8601 url parameters cli rest api contact us blog github search get started install rill developer agentic quickstart quickstart why rill build your first project connectors models metrics views what are metrics views underlying model table derived metrics views time series dimensions measures measure formatting case statements and filters referencing measures quantiles fixed measures window functions annotations rollups data access control metrics sql dashboards custom apis alerts project configuration ai configuration organize your code files external ide integration debugging in rill developer deploy rill cloud vs rill developer configure deployment credentials deploy dashboards managing project errors embed iframe postmessage api tutorials other release notes home build metrics views measures on this page define your measures measures are the quantitative metrics that power your dashboards and reports they represent numeric calculations derived from your underlying data through sql aggregation functions and expressions these measures transform raw data into meaningful business insights such as total revenue average order value or customer count measures are the how much and how many of your data they provide the numerical foundation for your analysis enabling you to quantify performance track key business metrics like revenue growth and efficiency support decision making provide concrete numbers for business decisions supported sql functions standard sql numeric operators and functions common sql aggregates avg count max min sum stddev variance advanced aggregates engine dependent approx_count_distinct approx_quantile stddev_pop stddev_samp var_pop var_samp filtered aggregates can be used to filter the set of rows fed to the aggregate functions syntax may vary by engine engine specific sql dialects different olap engines support varying sql dialects and functions while standard sql functions work across engines some advanced features may be engine specific for engine specific documentation see duckdb duckdb sql documentation clickhouse clickhouse sql documentation druid druid sql documentation pinot pinot sql documentation as an example if you have a table of sales events with the sales price and customer id you could calculate the following measures with these aggregates and expressions number of sales count total revenue sum sales_price revenue per customer cast sum sales_price as float cast count distinct customer_id as float number of orders with order value more than 100 count filter where order_val 100 syntax may vary by engine explore these advanced capabilities to enhance your measures measure formatting learn how to format and display your measures effectively case statements and filters use conditional logic and filtering in your measures referencing measures reference and combine existing measures in your calculations quantiles calculate percentiles and quantiles for statistical analysis fixed measures create measures with fixed values and constants window functions apply window functions for advanced analytical operations previous query time joins next measure formatting supported sql functions 2026 rill data inc privacy policy terms of service community policy contributing